Estimating motion and structure from line matches: performance obtained and beyond

The performance issues of estimating motion and structure from line correspondences are studied. An approach to optimal estimation of motion and structure using line correspondences is presented. To minimize the expected errors in the estimated parameters, it is necessary to minimize the matrix-weighted discrepancy between the computed lines and the observed lines. In order to reliably reach the global minimum solution, a closed-form solution is computed and then used as the initial starting condition for an iterative optimal estimation algorithm. Simulation results show that, in the presence of noise, the accuracy of the optimal solution is not only considerably better than that of the closed-form solutions, but it has also reached a level that it is comparable with that of point-based optimal algorithms. Simulations also show that the error of the optimal solution is close to a theoretical lower error bound, the Cramer-Rao bound, which implies that there exists little room for accuracy improvement beyond the performance obtained